SOTAVerified

Sentiment Analysis

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Further readings:

Papers

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Showing 2051–2100 of 5630 papers

TitleStatusHype
A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets—0
Extraction of Russian Sentiment Lexicon for Product Meta-Domain—0
FaBERT: Pre-training BERT on Persian Blogs—0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities—0
A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT—0
A Data-driven Neural Network Architecture for Sentiment Analysis—0
CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter—0
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases—0
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach—0
CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation—0
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis—0
A Sentiment Analysis Approach to the Prediction of Market Volatility—0
A Combined Pattern-based and Distributional Approach for Automatic Hypernym Detection in Dutch.—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
Extracting word lists for domain-specific implicit opinions from corpora—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
A Sentiment-aligned Topic Model for Product Aspect Rating Prediction—0
Extracting Aspects Hierarchies using Rhetorical Structure Theory—0
A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents—0
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency—0
A Leveled Reading Corpus of Modern Standard Arabic—0
Extracting Aspect Specific Opinion Expressions—0
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention—0
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation—0
A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations—0
CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
A Database of Attribution Relations—0
Extracting Aspects and Polarity from Patents—0
Extracting Definitions and Hypernym Relations relying on Syntactic Dependencies and Support Vector Machines—0
Cloze-driven Pretraining of Self-attention Networks—0
A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
A Combined CNN and LSTM Model for Arabic Sentiment Analysis—0
ClimaText: A Dataset for Climate Change Topic Detection—0
A semantic-affective compositional approach for the affective labelling of adjective-noun and noun-noun pairs—0
A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic.—0
Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach—0
Extracting Emotion Phrases from Tweets using BART—0
Fake news stance detection using stacked ensemble of classifiers—0
Feature based Sentiment Analysis using a Domain Ontology—0
FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN—0
CLex: A Lexicon for Exploring Color, Concept and Emotion Associations in Language—0
CLEAR: Contrastive Learning for Sentence Representation—0
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification—0
Class Vectors: Embedding representation of Document Classes—0
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences—0
A Large Wordnet-based Sentiment Lexicon for Polish—0
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All datasetsSST-2 Binary classificationIMDbSST-5 Fine-grained classificationYelp Binary classificationMRYelp Fine-grained classificationBanglaBookDynaSentSST-3Sentiment MergedUser and product informationAmazon Review Full

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100—Unverified
2MT-DNN-SMARTAccuracy97.5—Unverified
3T5-11BAccuracy97.5—Unverified
4MUPPET Roberta LargeAccuracy97.4—Unverified
5T5-3BAccuracy97.4—Unverified
6ALBERTAccuracy97.1—Unverified
7StructBERTRoBERTa ensembleAccuracy97.1—Unverified
8XLNet (single model)Accuracy97—Unverified
9SMARTRoBERTaDev Accuracy96.9—Unverified
10ELECTRAAccuracy96.9—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68—Unverified
2RoBERTa-largeAccuracy96.54—Unverified
3XLNetAccuracy96.21—Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2—Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1—Unverified
6GraphStarAccuracy96—Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94—Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92—Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9—Unverified
10BERT large finetune UDAAccuracy95.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27—Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2—Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8—Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1—Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07—Unverified
6Heinsen Routing + GPT-2Accuracy58.5—Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2—Unverified
8BERT LargeAccuracy55.5—Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9—Unverified
10BCN+ELMoAccuracy54.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88—Unverified
2SVDCNNError4.74—Unverified
3LEAMError4.69—Unverified
4fastText, h=10, bigramError4.3—Unverified
5SWEM-hierError4.19—Unverified
6SRNNError3.96—Unverified
7M-ACNNError3.89—Unverified
8DNC+CUWError3.6—Unverified
9CCCapsNetError3.52—Unverified
10Block-sparse LSTMError3.27—Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500—Unverified
2VLAWEAccuracy93.3—Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5—Unverified
4AnglE-LLaMA-7BAccuracy91.09—Unverified
5byte mLSTM7Accuracy86.8—Unverified
6MEANAccuracy84.5—Unverified
7RNN-CapsuleAccuracy83.8—Unverified
8Capsule-BAccuracy82.3—Unverified
9SuBiLSTM-TiedAccuracy81.6—Unverified
10USE_T+CNNAccuracy81.59—Unverified